48 lines
2.0 KiB
Python
48 lines
2.0 KiB
Python
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import torch
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from torch import nn
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from src.nets.deepsets import DeepSetsModule, Phi
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class Policy:
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pass
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class DeepSetsPolicy(Policy, nn.Module):
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def __init__(self, ego_config, deepsets_config, path_config, head_config):
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"""
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Args:
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ego_config (dict): dictionary for configuring the ego network
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deepsets_config (dict): dictionary for configuring the deepsets network
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path_config (dict): dictionary for configuring the path network
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head_config (dict): dictionary for configuring the common head network
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"""
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super(DeepSetsPolicy, self).__init__()
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self.ego_net = Phi.from_config(ego_config)
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self.deepsets_net = DeepSetsModule.from_config(deepsets_config)
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self.path_net = Phi.from_config(path_config)
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cat_dim = self.ego_net.output_dim + self.deepsets_net.output_dim + self.path_net.output_dim
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# head has number of concatenated features as input
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head_config["input_dim"] = cat_dim
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self.head = Phi.from_config(head_config)
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def forward(self, sample):
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"""
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Args:
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sample (dict): sample dictionary with the following entries:
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state (torch.tensor): (B, 5) raw state
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relative_state (torch.tensor): (B, max_nv, d) relative state (padded with nans)
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path_x (torch.tensor): (B, P) tensor of P future path x positions
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path_y (torch.tensor): (B, P) tensor of P future path y positions
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action (torch.tensor): (B, 1) actions taken from each state
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Returns:
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x (torch.tensor): (head_output_dim,) output of common head network
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"""
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ego = self.ego_net(sample["state"])
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relative = self.deepsets_net(sample["relative_state"])
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# cat path_x, path_y to tensor of dim (B, 2*P)
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path = torch.cat([sample["path_x"], sample["path_y"]], dim=-1)
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path = self.path_net(path)
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x = torch.cat([ego, relative, path])
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x = self.head(x)
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return x
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